I am a machine learning researcher with a background in mathematics and physics. I worked on natural language processing at Rasa, Amazon, and Lilt, with a particular focus on natural language processing.

To me, the rapid progress in deep learning theory, which is now catching up with modern practice, is the most interesting ongoing development in my field.

Curriculum Vitae

2023 - presentWorkResearch Scientist IIILilt
2023StudyTheoretical Physics for Deep Learning workshopAspen Center for Physics
2022 - 2023WorkResearch Scientist II, AlexaAmazon
2019 - 2022WorkMachine learning researcherRasa
2021AwardGavin Brown Best Paper Prize (contributor, shared)Australian Mathematical Society
2018WorkSoftware developerFGEU mbH
2018TeachTeaching fellowUniversity of Otago
2018AwardExceptional Thesis AwardUniversity of Otago
2014 - 2018StudyPhD in mathematicsUniversity of Otago
2018TeachTutor for undergraduate physicsUniveristy of Otago’s Disability Information
and Support Office
2016 - 2017WorkTechnology evangelistWolfram Research
2015 - 2017TeachTutor for mathematicsUniversity of Otago
2011 - 2014StudyMSc in physicsUniversity of Potsdam
Max Planck Institute for Gravitational Physics
2012 - 2014WorkSoftware developerFGEU mbH
2011 - 2013TeachTutor for undergraduate physicsUniversity of Potsdam
2012StudyComplex Quantum Systems (CoQuS) summer schoolUniversity of Vienna
2011StudyDAAD, RISE in North America programmeYork University


The following list does not include blog posts, minor conference papers or presentations.

Machine Learning

Mosig, J. E. M., Mehri, S., & Kober, T. (2020). STAR: A Schema-Guided Dialog Dataset for Transfer Learning. ArXiv:2010.11853 [Cs].
Mosig, J. E. M., Vlasov, V., & Nichol, A. (2020). Where is the context? -- A critique of recent dialogue datasets. ArXiv:2004.10473 [Cs].
Vlasov, V., Mosig, J. E. M., & Nichol, A. (2019). Dialogue Transformers. ArXiv:1910.00486 [Cs].


Fräßdorf, C., & Mosig, J. E. M. (2017). Chemical-potential flow equations for graphene with Coulomb interactions. ArXiv Preprint ArXiv:1707.03920.
Fräßdorf, C., & Mosig, J. E. M. (2017). Keldysh functional renormalization group for electronic properties of graphene. Physical Review B, 95(12). https://doi.org/10.1103/PhysRevB.95.125412
Mosig, J. E. M. (2014). Spin Fields and Hidden Symmetries in Curved Spacetime [Master Thesis]. University of Potsdam.
Mosig, J. E. M. (2011). Electromagnetic Processes with Hadronic Final State Pion, Kaon, Antikaon [Bachelor Thesis]. Freie Universität Berlin.

Sea Ice & Ocean Modeling

Meylan, M. H., Bennetts, L. G., Mosig, J. E. M., Rogers, W. E., Doble, M. J., & Peter, M. A. (2018). Dispersion Relations, Power Laws, and Energy Loss for Waves in The Marginal Ice Zone. Journal of Geophysical Research: Oceans. https://doi.org/10.1002/2018JC013776
Mosig, J. E. M. (2018). Contemporary wave–ice interaction models [PhD, University of Otago].
Mosig, J. E. M., Montiel, F., & Squire, V. A. (2016). Water wave scattering from a mass loading ice floe of random length using generalised polynomial chaos. Wave Motion. https://doi.org/10.1016/j.wavemoti.2016.09.005
Mosig, J. E. M., Montiel, F., & Squire, V. A. (2015). Comparison of viscoelastic-type models for ocean wave attenuation in ice-covered seas. Journal of Geophysical Research: Oceans, 120(9), 6072–6090. https://doi.org/10.1002/2015JC010881
Mosig, J. E. M., Montiel, F., & Squire, V. A. (2015, September). Rheological models of flexural-gravity waves in an ice covered ocean on large scales. 7th International Conference on Hydroelasticity in Marine Technology. HYEL 2015, Split, Croatia.

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Last Update: 2023-03-09